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arXiv 2025年

作者： Huang, Dehao Dong, Wenlong Tang, Chao Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Task-oriented grasping (TOG) is essential for robots to perform manipulation tasks, requiring grasps that are both stable and compliant with task-specific constraints. Humans naturally grasp objects in a task-oriented manner to facilitate subsequent manipulation tasks. By leveraging human grasp demonstrations, current methods can generate high-quality robotic parallel-jaw task-oriented grasps for diverse objects and tasks. However, they still encounter challenges in maintaining grasp stability and sampling efficiency. These methods typically rely on a two-stage process: first performing exhaustive task-agnostic grasp sampling in the 6-DoF space, then applying demonstration-induced constraints (e.g., contact regions and wrist orientations) to filter candidates. This leads to inefficiency and potential failure due to the vast sampling space. To address this, we propose the Human-guided Grasp Diffuser (HGDiffuser), a diffusion-based framework that integrates these constraints into a guided sampling process. Through this approach, HGDiffuser directly generates 6-DoF task-oriented grasps in a single stage, eliminating exhaustive task-agnostic sampling. Furthermore, by incorporating Diffusion Transformer (DiT) blocks as the feature backbone, HGDiffuser improves grasp generation quality compared to MLP-based methods. Experimental results demonstrate that our approach significantly improves the efficiency of task-oriented grasp generation, enabling more effective transfer of human grasping strategies to robotic systems. To access the source code and supplementary videos, visit https://***/ view/hgdiffuser. Copyright © 2025, The Authors. All rights reserved.

关键词： Robotics

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A Multi-Agent Digital Twin Framework for AI-Driven Fitness Coaching 25

A Multi-Agent Digital Twin Framework for AI-Driven Fitness C...

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Proceedings of the 2025 ACM International Conference on Interactive Media Experiences

作者： Monica (Monireh) Vahdati Kamran Gholizadeh HamlAbadi Fedwa Laamarti Abdulmotaleb El Saddik Multimedia Communications Research Laboratory (MCRLab) School of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario Canada Computer Vision Department Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi United Arab Emirates and Multimedia Communications Research Laboratory (MCRLab) School of Electrical Engineering and Computer Science University of Ottawa Ottawa Ontario Canada

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When SAM2 meets video camouflaged object segmentation: a comprehensive evaluation and adaptation

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Visual Intelligence 2025年第1期3卷 1-14页

作者： Zhou, Yuli Sun, Guolei Li, Yawei Xie, Guo-Sen Benini, Luca Konukoglu, Ender Computer Vision Laboratory ETH Zürich Zürich Switzerland Department of Informatics University of Zürich Zürich Switzerland Integrated System Laboratory ETH Zürich Zürich Switzerland School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Electrical Electronic and Information Engineering University of Bologna Bologna Italy

This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS). VCOS involves detecting objects that blend seamlessly in the surroundings for videos due to similar colors and textures and poor light conditions. Compared to the objects in normal scenes, camouflaged objects are much more difficult to detect. SAM2, a video foundation model, has shown potential in various tasks. However, its effectiveness in dynamic camouflaged scenarios remains under-explored. This study presents a comprehensive study on SAM2’s ability in VCOS. First, we assess SAM2’s performance on camouflaged video datasets using different models and prompts (click, box, and mask). Second, we explore the integration of SAM2 with existing multimodal large language models (MLLMs) and VCOS methods. Third, we specifically adapt SAM2 by fine-tuning it on the video camouflaged dataset. Our comprehensive experiments demonstrate that SAM2 has the excellent zero-shot ability to detect camouflaged objects in videos. We also show that this ability could be further improved by specifically adjusting SAM2’s parameters for VCOS.

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computer vision Approaches for Automated Bee Counting Application

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IFAC-PapersOnLine 2024年第9期58卷 43-48页

作者： Simon Bilik Ilona Janakova Adam Ligocki Dominik Ficek Karel Horak Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic and Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic

Many application from the bee colony health state monitoring could be efficiently solved using a computer vision techniques. One of such challenges is an efficient way for counting the number of incoming and outcoming bees, which could be used to further analyse many trends, such as the bee colony health state, blooming periods, or for investigating the effects of agricultural spraying. In this paper, we compare three methods for the automated bee counting over two own datasets. The best performing method is based on the ResNet-50 convolutional neural network classifier, which achieved accuracy of 87% over the BUT1 dataset and the accuracy of 93% over the BUT2 dataset.

关键词： Signal Processing Biomedical systems bee counting bee traffic monitoring computer vision deep classification object detection

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Efficient Object Rearrangement via Multi-view Fusion

Efficient Object Rearrangement via Multi-view Fusion

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Dehao Huang Chao Tang Hong Zhang Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The prospect of assistive robots aiding in object organization has always been compelling. In an image-goal setting, the robot rearranges the current scene to match the single image captured from the goal scene. The key to an image-goal rearrangement system is estimating the desired placement pose of each object based on the single goal image and observations from the current scene. In order to establish sufficient associations for accurate estimation, the system should observe an object from a viewpoint similar to that in the goal image. Existing image-goal rearrangement systems, due to their reliance on a fixed viewpoint for perception, often require redundant manipulations to randomly adjust an object’s pose for a better perspective. Addressing this inefficiency, we introduce a novel object rearrangement system that employs multi-view fusion. By observing the current scene from multiple viewpoints before manipulating objects, our approach can estimate a more accurate pose without redundant manipulation times. A standard visual localization pipeline at the object level is developed to capitalize on the advantages of multi-view observations. Simulation results demonstrate that the efficiency of our system outperforms existing single-view systems. The effectiveness of our system is further validated in a physical experiment. For videos, please visit https: //***/view/multi-view-rearr.

关键词： Visualization Accuracy Databases Simulation Standards organizations Pose estimation Pipelines

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Optimizing NeRF-based SLAM with Trajectory Smoothness Constraints

arXiv

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arXiv 2024年

作者： He, Yicheng Chen, Guangcheng Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

The joint optimization of Neural Radiance Fields (NeRF) and camera trajectories has been widely applied in SLAM tasks due to its superior dense mapping quality and consistency. NeRF-based SLAM learns camera poses using constraints by implicit map representation. A widely observed phenomenon that results from the constraints of this form is jerky and physically unrealistic estimated camera motion, which in turn affects the map quality. To address this deficiency of current NeRF-based SLAM, we propose in this paper TS-SLAM (TS for Trajectory Smoothness). It introduces smoothness constraints on camera trajectories by representing them with uniform cubic B-splines with continuous acceleration that guarantees smooth camera motion. Benefiting from the differentiability and local control properties of B-splines, TS-SLAM can incrementally learn the control points end-to-end using a sliding window paradigm. Additionally, we regularize camera trajectories by exploiting the dynamics prior to further smooth trajectories. Experimental results demonstrate that TS-SLAM achieves superior trajectory accuracy and improves mapping quality versus NeRF-based SLAM that does not employ the above smoothness constraints. © 2024, CC BY.

关键词： Mapping

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Analyzing Infrared Linescan Profiles of Steel Strips for Enhanced Cooling Pattern Prediction

Analyzing Infrared Linescan Profiles of Steel Strips for Enh...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Rubén Usamentiaga Adrian G. Gayo Francisco J. DelaCalle Dario G. Lema Stefano Sfarra Hai Zhang Department of Computer Science and Engineering University of Oviedo Gijón Asturias Spain Department of Industrial and Information Engineering and Economics (DIIIE) University of L'Aquila L'Aquila Italy Department of Electrical and Computer Engineering Computer Vision and Systems Laboratory (CVSL) Laval University Quebec Canada

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Real-time temperature information is crucial for optimizing cooling processes during steel strip rolling, ensuring the attainment of desired microstructural properties and surface quality at an optimal cooling rate. Infrared line scanners emerge as the preferred choice for temperature measurement in highspeed rolling operations, delivering temperature readings with high resolution and enabling the capture of detailed temperature profiles. By analyzing these profiles, cooling systems can be finely adjusted and precisely controlled to optimize the rolling operation. However, developing effective cooling strategies becomes challenging when dealing with temperature profiles comprising numerous discrete data points, often numbering in the thousands per profile. This study presents an innovative approach that integrates the detection of steel strip boundaries within temperature profiles and subsequent temperature pattern characterization using polynomial fitting. A significant advantage is demonstrated by leveraging the coefficients of Legendre polynomials, which provide a concise description of temperature profile shapes, facilitating straightforward approaches to cooling strategies. By integrating boundary detection with temperature characterization, the system enhances its ability to predict tailored cooling patterns, optimizing cooling efficiency, and enhancing product quality in the manufacturing process. Rigorous testing using both synthetic data and real-world applications in cold and hot rolling validates the proposed system's practical utility and reliability. These results underscore its potential to enhance efficiency and quality in industrial steel manufacturing operations.

关键词： Temperature measurement Temperature sensors Strips Cooling Shape Polynomials Real-time systems Steel Monitoring Testing

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RTAGrasp: Learning Task-Oriented Grasping from Human Videos via Retrieval, Transfer, and Alignment

arXiv

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arXiv 2024年

作者： Dong, Wenlong Huang, Dehao Liu, Jiangshan Tang, Chao Zhang, Hong Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Task-oriented grasping (TOG) is crucial for robots to accomplish manipulation tasks, requiring the determination of TOG positions and directions. Existing methods either rely on costly manual TOG annotations or only extract coarse grasping positions or regions from human demonstrations, limiting their practicality in real-world applications. To address these limitations, we introduce RTAGrasp, a Retrieval, Transfer, and Alignment framework inspired by human grasping strategies. Specifically, our approach first effortlessly constructs a robot memory from human grasping demonstration videos, extracting both TOG position and direction constraints. Then, given a task instruction and a visual observation of the target object, RTAGrasp retrieves the most similar human grasping experience from its memory and leverages semantic matching capabilities of vision foundation models to transfer the TOG constraints to the target object in a training-free manner. Finally, RTAGrasp aligns the transferred TOG constraints with the robot’s action for execution. Evaluations on the public TOG benchmark, TaskGrasp dataset, show the competitive performance of RTAGrasp on both seen and unseen object categories compared to existing baseline methods. Real-world experiments further validate its effectiveness on a robotic arm. Our code, appendix, and video are available at https: //***/view/rtagrasp/home. © 2024, CC BY.

关键词： Robotic arms

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Enhancing Micro Gesture Recognition for Emotion Understanding via Context-aware Visual-Text Contrastive Learning

arXiv

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arXiv 2024年

作者： Li, Deng Xing, Bohao Liu, Xin School of Electrical and Information Engineering Tianjin University Tianjin300072 China Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta53850 Finland

Psychological studies have shown that Micro Gestures (MG) are closely linked to human emotions. MG-based emotion understanding has attracted much attention because it allows for emotion understanding through nonverbal body gestures without relying on identity information (e.g., facial and electrocardiogram data). Therefore, it is essential to recognize MG effectively for advanced emotion understanding. However, existing Micro Gesture Recognition (MGR) methods utilize only a single modality (e.g., RGB or skeleton) while overlooking crucial textual information. In this letter, we propose a simple but effective visual-text contrastive learning solution that utilizes text information for MGR. In addition, instead of using handcrafted prompts for visual-text contrastive learning, we propose a novel module called Adaptive prompting to generate context-aware prompts. The experimental results show that the proposed method achieves state-of-the-art performance on two public datasets. Furthermore, based on an empirical study utilizing the results of MGR for emotion understanding, we demonstrate that using the textual results of MGR significantly improves performance by 6%+ compared to directly using video as input. Copyright © 2024, The Authors. All rights reserved.

关键词： Gesture recognition

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Multi-Scale Point Octree Encoding Network for Point Cloud Based Place Recognition

Multi-Scale Point Octree Encoding Network for Point Cloud Ba...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zhilong Tang Hanjing Ye Hong Zhang Department of Electrical and Electronic Engineering Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology (SUSTech) SUSTech Shenzhen China

Over the past decades, point cloud-based place recognition has garnered significant attention. This research paper presents a pioneering approach, denoted as the Multi-scale Point Octree Encoding Network (MPOE-Net), designed to acquire a discriminative global descriptor for efficient retrieval of places. The key element of the MPOE-Net is the point octree encoding module, which adeptly captures local information for each point by considering its nearest and farthest neighbors. Further enhancing local relationships, a multi-transformer network is introduced, utilizing a novel grouped offset-attention mechanism. To amalgamate the multi-scale attention maps into a comprehensive global descriptor, a multi-NetVLAD layer is incorporated. Through rigorous experimentation across diverse benchmark datasets, our proposed method unequivocally outperforms existing techniques in the realm of point cloud-based place recognition tasks, achieving state-of-the-art results. Our code is released publicly at https://***/Zhilong-Tang/MPOE-Net.

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